System and method for precision oral medication delivery based on real-time health monitoring

Abstract

The present invention discloses a system and method for precision oral medication delivery based on real-time health monitoring. The system comprises a palatal medication delivery device, a plurality of sensors configured to measure real-time health parameters of a user, a precision medication control unit comprising a plurality of subsystems comprising a data processing subsystem configured to receive the measured real-time health parameters, filter out noise from the received real-time health parameters, normalize the noise-free health parameters, extract health features from the normalized noise-free data, an artificial intelligence (AI) subsystem configured to determine health patterns based on the extracted health features and compare the determined health patterns with a predefined baseline to detect a deviation, a dosage prediction subsystem configured to determine a precision dosage instruction, upon detection of the deviation and the digital controller unit configured to release a microfluidic dosage of medication based on the precision dosage instruction.

We claim: 
     
         1 . A system for precision oral medication delivery based on real-time health monitoring, the system comprising:
 a palatal medication delivery device comprising:
 a biocompatible layer; 
 a medication holding chamber comprising a plurality of micro-holes that are distributed across surface of the medication holding chamber; and 
 a digital controller unit connected with the medication holding chamber configured to control release of medication through the plurality of micro-holes; 
   a plurality of sensors operably coupled to the palatal medication delivery device configured to measure real-time health parameters of a user,
 wherein the real-time health parameters comprise at least one of a body temperature parameter, a heart rate parameter, an oxygen saturation level parameter, and a salivary biomarker parameter; 
   a precision medication control unit operably coupled to the palatal medication delivery device, comprising:
 one or more hardware processors; and 
 a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in the form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises:
 a data processing subsystem configured to:
 receive the measured real-time health parameters; 
 filter out, by one or more filtering techniques, noise from the received real-time health parameters; 
 normalize, by one or more normalization techniques, the noise-free health parameters; 
 extract, by one or more feature extraction techniques, health features from the normalized noise-free data; 
 
 an artificial intelligence (AI) subsystem configured to:
 determine, by one or more neural networks, health patterns based on the extracted health features, 
  wherein the determined health patterns comprise at least one of a heart rate variability pattern, a glucose level fluctuation pattern, a physical activity pattern, a respiratory pattern, and a temperature level pattern, 
  wherein the determined health patterns indicate the real-time health status of the user; and 
 compare the determined health patterns with a predefined baseline to detect a deviation in the real-time health status of the user; 
 
 a dosage prediction subsystem configured to determine a precision dosage instruction, upon detection of the deviation in the real-time health status of the user; and 
 
   the digital controller unit configured to release a microfluidic dosage of medication based on the precision dosage instruction.   
     
     
         2 . The system as claimed in  claim 1 , wherein the biocompatible layer is made of at least one of a medical-grade silicone, biocompatible polymers, and hydrogel. 
     
     
         3 . The system as claimed in  claim 1 , wherein the digital controller unit is configured to adjust the rate of medication released through the micro-holes, based on the precision dosage instruction. 
     
     
         4 . The system as claimed in  claim 1 , wherein the plurality of sensors comprises at least one of:
 a temperature sensor for measuring body temperature;   a photoplethysmography (PPG) sensor for measuring heart rate and oxygen saturation levels;   an electrochemical sensor for detecting salivary biomarkers including glucose level, cortisol level, and electrolyte level; and   an inertial measurement unit (IMU) sensor for tracking physical activity and movement patterns.   
     
     
         5 . The system as claimed in  claim 1 , wherein the dosage prediction subsystem is configured to determine the precision dosage instruction, upon detection of the deviation in the real-time health status of the user, further comprises:
 receiving a plurality of historical patient data from a patient database,
 wherein the historical patient data includes at least one of a historical health parameter of the user, a historical medication dosage data, and a treatment response to the medication; 
   determine, by one or more learning models, a medication dosage instruction based on the real-time health status of the user and the plurality of historical patient data;   optimize the medication dosage instruction to determine a precision dosage instruction for the user, based on a plurality of user-specific parameters,
 wherein the user-specific parameters include at least one of an age, a weight, and a metabolism, and 
 wherein the precision dosage instruction comprises one of a precise amount of medication to be released, a rate of release of the medication, and a timing of release of the medication. 
   
     
     
         6 . The system as claimed in  claim 1 , further comprises a user interface operably connected to the precision medication control unit configured to display real-time health status and medication dosage information to the user. 
     
     
         7 . The system as claimed in  claim 1 , further comprises a wireless communication unit configured to transmit real-time health data and dosage information to a healthcare provider. 
     
     
         8 . The system as claimed in  claim 1 , wherein the one or more filtering techniques comprise at least one of a Kalman filtering technique and a Butterworth filtering technique. 
     
     
         9 . The system as claimed in  claim 1 , wherein the one or more normalization techniques comprise at least one of a Min-Max Normalization and Z-Score Normalization. 
     
     
         10 . The system as claimed in  claim 1 , wherein the one or more feature extraction techniques comprise at least one of a Fast Fourier Transform (FFT) and a wavelet analysis. 
     
     
         11 . The system as claimed in  claim 1 , wherein the one or more neural networks comprise at least one of a convolutional neural network (CNN) and a recurrent neural network (RNN). 
     
     
         12 . The system as claimed in  claim 5 , wherein the one or more learning models comprise at least one of a supervised learning model and an reinforcement learning model, wherein the supervised learning model includes at least one of a Random Forests model and a Support Vector Machines model. 
     
     
         13 . A method for precision oral medication delivery based on real-time health monitoring, the method comprising:
 measuring, by a plurality of sensors, real-time health parameters of a user operably coupled to a palatal medication delivery device,
 wherein the real-time health parameters comprise at least one of a body temperature parameter, a heart rate parameter, an oxygen saturation level parameter, and a salivary biomarker parameter; 
   receiving, by a data processing subsystem, the measured real-time health parameters;   filtering out, by one or more filtering techniques, noise from the received real-time health parameters;   normalizing, by one or more normalization techniques, the noise-free health parameters;   extracting, by one or more feature extraction techniques, health features from the normalized noise-free data;   determining, by one or more neural network, health patterns based on the extracted health features,
 wherein the determined health patterns comprise at least one of a heart rate variability pattern, a glucose level fluctuation pattern, an activity pattern, a respiratory pattern, and a temperature level pattern, and 
 wherein the determined health patterns indicate the real-time health status of the user; 
   comparing, by an artificial intelligence (AI) subsystem, the determined health patterns with a predefined baseline to detect a deviation in the real-time health status of the user;   determining, by a dosage prediction subsystem, a precision dosage instruction upon detection of the deviation in the real-time health status of the user;   releasing, by the digital controller unit, a microfluidic dosage of medication based on the precision dosage instruction.   
     
     
         14 . The method as claimed in  claim 13 , wherein the palatal medication delivery device comprises a biocompatible layer made of at least one of a medical-grade silicone, biocompatible polymers, and hydrogel. 
     
     
         15 . The method as claimed in  claim 13 , further comprising:
 adjusting, by the digital controller unit, the rate of medication released through the micro-holes based on the precision dosage instruction.   
     
     
         16 . The method as claimed in  claim 13 , wherein the plurality of sensors comprise at least one of:
 a temperature sensor for measuring body temperature;   a photoplethysmography (PPG) sensor for measuring heart rate and oxygen saturation levels;   an electrochemical sensor for detecting salivary biomarkers including glucose level, cortisol level, and electrolyte level; and   an inertial measurement unit (IMU) sensor for tracking physical activity and movement patterns.   
     
     
         17 . The method as claimed in  claim 13 , wherein determining the precision dosage instruction, upon detection of the deviation in the real-time health status of the user, further comprises:
 receiving a plurality of historical patient data from a patient database,
 wherein the historical patient data includes at least one of a historical health parameter of the user, a historical medication dosage data, and a treatment response to the medication; 
   determining, by one or more learning model, a medication dosage instruction based on the real-time health status and the historical patient data; and   optimizing the medication dosage instruction to determine a precision dosage instruction for the user, based on a plurality of user-specific parameters,
 wherein the user-specific parameters include at least one of an age, a weight, and a metabolism, and wherein the precision dosage instruction comprises at least one of a precise amount of medication to be released, a rate of release of the medication, and a timing of release of the medication. 
   
     
     
         18 . The method as claimed in  claim 13 , further comprising:
 displaying, via a user interface operably connected to a precision medication control unit, real-time health parameters and dosage information to the user.   
     
     
         19 . The method as claimed in  claim 13 , further comprising:
 transmitting, via a wireless communication unit, real-time health data and dosage information to a healthcare provider.   
     
     
         20 . The method as claimed in  claim 13 , wherein the one or more filtering techniques comprise at least one of a Kalman filtering technique and a Butterworth filtering technique. 
     
     
         21 . The method as claimed in  claim 13 , wherein the one or more normalization techniques comprise at least one of a Min-Max Normalization and Z-Score Normalization. 
     
     
         22 . The method as claimed in  claim 13 , wherein the one or more feature extraction techniques comprise at least one of a Fast Fourier Transform (FFT) and a wavelet analysis. 
     
     
         23 . The method as claimed in  claim 13 , wherein the one or more neural networks comprise at least one of a convolutional neural network (CNN) and a recurrent neural network (RNN). 
     
     
         24 . The method as claimed in  claim 17 , wherein the one or more learning models comprise at least one of a supervised learning model and a reinforcement learning model, wherein the supervised learning model includes at least one of a Random Forests model and a Support Vector Machines model.